Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions

Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-RKHS) formulate the kernel learning task as learning on the space of kernels itself, which provides significant model flexibility for kernel learning with outstanding performance in real-world applications. However, the convergence behavior of these learning algorithms in hyper-RKHS has not been investigated in learning theory... (read more)

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